Google's Gemini 4 Argon scores 87% on physical reasoning test
The model is aimed at robot control, code generation and visual reasoning.
87%Score on ERQA physical reasoning benchmark
The short version
- Google DeepMind says Gemini 4 Argon marks a step change in physical understanding, code generation, and visual-semantic reasoning.
- On the Embodied Reasoning (ERQA) benchmark, which tests physical and spatial problems in 3D environments, Argon scores 87% using pure reasoning, which the post says outperforms every other frontier model.
- The post shows Argon controlling a Boston Dynamics Spot quadruped to step on a foot-pedal trash bin and drop in soda cans.
- Argon also controls a simple robotic arm zero-shot to place a fallen mug upright and put a crumpled napkin inside, and it steers the Gemini Omni World Model to rearrange Scrabble tiles.
Why it matters
The post describes Argon as a general-purpose physical reasoning model that can be applied across the entire robotics lifecycle, which it links to progress toward physical AGI. For developers, the examples cover digital twins built in MuJoCo and real-to-sim replay of teleoperation episodes.
“As a general-purpose physical reasoning model, Argon can be applied across the entire robotics lifecycle.”
What people are saying
Commenters on Hacker News questioned whether a model like this could run on battery power in a robot of practical size. One said the zero-shot control approach suits many simple robot tasks, and another called the results mildly impressive, saying Google should be ahead of its competitors given its resources.